Deep Convolutional Neural Network for Semantic Segmentation

Semantic segmentation, the understanding of scenes from input images, is an important task in computer vision. While object recognition estimates an object category whole image, semantic segmentation estimates an object category for each pixel. Semantic segmentation, in particular, SegNet and Fully Convolutional Network (FCN) are representative semantic segmentation methods.

Multiple Dilated Convolution Blocks for Semantic Segmentation
Object scales on vehicle camera varies with the distance between camera and object such as pedestrian or vehicle. Therefore, we propose Multiple Dilated Convolution Blocks, which corresponds to various object scales. Our approach can extract both global and local context. When we evaluated our approach using the Cityscape Dataset benchmark, we outperformed conventionally semantic segmentation methods such as FCN and SegNet.